Analyzing data streams using a dynamic compact stream pattern algorithm

Oyewale, A, Hughes, CJ and Saraee, MH ORCID: 0000-0002-3283-1912 2018, Analyzing data streams using a dynamic compact stream pattern algorithm , in: The Eighth International Conference on Advances in Information Mining and Management, 22-26 July 2018, Barcelona, Spain.

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Abstract

In order to succeed in the global competition, organizations need to understand and monitor the rate of data influx. The acquisition of continuous data has been extremely outstretched as a concern in many fields. Recently, frequent patterns in data streams have been a challenging task in the field of data mining and knowledge discovery. Most of these datasets generated are in the form of a stream (stream data), thereby posing a challenge of being continuous. Therefore, the process of extracting knowledge structures from continuous rapid data records is termed as stream mining. This study conceptualizes the process of detecting outliers and responding to stream data. This is done by proposing a Compressed Stream Pattern algorithm, which dynamically generates a frequency descending prefix tree structure with only a singlepass over the data. We show that applying tree restructuring techniques can considerably minimize the mining time on various datasets.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre (SIRC)
Journal or Publication Title: Proceedings, The Eighth International Conference on Advances in Information Mining and Management (IMMM 2018)
Publisher: International Academy, Research, and Industry Association (IARIA)
Related URLs:
Depositing User: Dr Mo Saraee
Date Deposited: 06 Aug 2018 08:48
Last Modified: 04 Sep 2018 14:56
URI: http://usir.salford.ac.uk/id/eprint/47969

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